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<a href="_kernel_budgeted_s_g_d_trainer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Budgeted stochastic gradient descent training for kernel-based models.</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \par This is an implementation  of the BSGD algorithm, developed by</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *  Wang, Crammer and Vucetic: Breaking the curse of kernelization:</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *  Budgeted stochastic gradient descent for large-scale SVM training, JMLR 2012.</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * Basically this is pegasos, so something similar to a perceptron. The main</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * difference is that we do restrict the sparsity of the weight vector to a (currently</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * predefined) value. Therefore, whenever this sparsity is reached, we have to</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * decide how to add a new vector to the model, without destroying this</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * sparsity. Several methods have been proposed for this, Wang et al. main</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * insight is that merging two budget vectors (i.e. two vectors in the model).</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * If the first one is searched by norm of its alpha coefficient, the second one</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * can be found by some optimization problem, yielding a roughly optimal pair.</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * This pair can be merged and by doing so the budget has now space for a</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * new vector. Such strategies are called budget maintenance strategies.</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> *</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * \par This implementation owes much to the &#39;reference&#39; implementation</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * in the BudgetedSVM software.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> *</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> *</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * \author      T. Glasmachers, Aydin Demircioglu</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * \date        2014</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> *</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> *</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment"> *</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment"> *</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment"> *</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment"> *</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment"> */</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span> </div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span> </div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_KERNELBUDGETEDSGDTRAINER_H</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="preprocessor">#define SHARK_ALGORITHMS_KERNELBUDGETEDSGDTRAINER_H</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span> </div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="preprocessor">#include &lt;iostream&gt;</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_budget_maintenance_strategy_8h.html">shark/Algorithms/Trainers/Budgeted/AbstractBudgetMaintenanceStrategy.h</a>&gt;</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span> </div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_trainer_8h.html" title="Abstract Trainer Interface.">shark/Algorithms/Trainers/AbstractTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="preprocessor">#include &lt;<a class="code" href="_k_means_8h.html">shark/Algorithms/KMeans.h</a>&gt;</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="preprocessor">#include &lt;<a class="code" href="_i_parameterizable_8h.html">shark/Core/IParameterizable.h</a>&gt;</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_matrix_8h.html">shark/LinAlg/KernelMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="preprocessor">#include &lt;<a class="code" href="_partly_precomputed_matrix_8h.html">shark/LinAlg/PartlyPrecomputedMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_expansion_8h.html">shark/Models/Kernels/KernelExpansion.h</a>&gt;</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_helpers_8h.html">shark/Models/Kernels/KernelHelpers.h</a>&gt;</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_loss_8h.html" title="super class of all loss functions">shark/ObjectiveFunctions/Loss/AbstractLoss.h</a>&gt;</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span> </div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span> </div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>{</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span> </div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment"></span> </div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">///</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment">/// \brief       Budgeted stochastic gradient descent training for kernel-based models.</span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment">///</span></div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="comment">/// \par This is an implementation  of the BSGD algorithm, developed by</span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment">///  Wang, Crammer and Vucetic: Breaking the curse of kernelization:</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment">///  Budgeted stochastic gradient descent for large-scale SVM training, JMLR 2012.</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">/// Basically this is pegasos, so something similar to a perceptron. The main</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">/// difference is that we do restrict the sparsity of the weight vector to a (currently</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">/// predefined) value. Therefore, whenever this sparsity is reached, we have to</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">/// decide how to add a new vector to the model, without destroying this</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">/// sparsity. Several methods have been proposed for this, Wang et al. main</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment">/// insight is that merging two budget vectors (i.e. two vectors in the model).</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="comment">/// If the first one is searched by norm of its alpha coefficient, the second one</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment">/// can be found by some optimization problem, yielding a roughly optimal pair.</span></div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment">/// This pair can be merged and by doing so the budget has now space for a</span></div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">/// new vector. Such strategies are called budget maintenance strategies.</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment">///</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment">/// \par This implementation owes much to the &#39;reference&#39; implementation</span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment">/// in the BudgetedSVM software.</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="comment">///</span></div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment">/// \par For the documentation of the basic SGD algorithm, please refer to</span></div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment">/// KernelSGDTrainer.h. Note that we did not take over the special alpha scaling</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment">/// from that class. Therefore this class is perhaps numerically not as robust as SGD.</span></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment">///</span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> CacheType = <span class="keywordtype">float</span>&gt;</div>
<div class="foldopen" id="foldopen00097" data-start="{" data-end="};">
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html">   97</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html" title="Budgeted stochastic gradient descent training for kernel-based models.">KernelBudgetedSGDTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_trainer.html" title="Superclass of supervised learning algorithms.">AbstractTrainer</a>&lt; KernelClassifier&lt;InputType&gt; &gt;, <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_parameterizable.html" title="Top level interface for everything that holds parameters.">IParameterizable</a>&lt;&gt;</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>{</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_trainer.html" title="Superclass of supervised learning algorithms.">AbstractTrainer&lt; KernelExpansion&lt;InputType&gt;</a> &gt; <a class="code hl_class" href="classshark_1_1_abstract_trainer.html" title="Superclass of supervised learning algorithms.">base_type</a>;</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a2e358ce3d8b3397c0f53bbf04d9c5493">  101</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a2e358ce3d8b3397c0f53bbf04d9c5493">KernelType</a>;</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aabd1a7c1a563d9c29039fabc8f1a57c2">  102</a></span>    <span class="keyword">typedef</span> <a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aabd1a7c1a563d9c29039fabc8f1a57c2">ClassifierType</a>;</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a62a15b8334075f72a3dfd0827d6d28e5">  103</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a62a15b8334075f72a3dfd0827d6d28e5">ModelType</a>;</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a06bca64eeb1641f17d435da555a78c22">  104</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html" title="Loss function interface.">AbstractLoss&lt;unsigned int, RealVector&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a06bca64eeb1641f17d435da555a78c22">LossType</a>;</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a1f29257150948ec40a72088b71509195">  105</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_struct" href="structshark_1_1_const_proxy_reference.html" title="sets the type of ProxxyReference">ConstProxyReference&lt;typename Batch&lt;InputType&gt;::type</a> <span class="keyword">const</span>&gt;::type <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a1f29257150948ec40a72088b71509195">ConstBatchInputReference</a>;</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a46a5cbca5391f2985a52e84ab9fa99fa">  106</a></span>    <span class="keyword">typedef</span> CacheType <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a46a5cbca5391f2985a52e84ab9fa99fa">QpFloatType</a>;</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5b834dccb79cf2bd9623ad3caf374aa5">  107</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> LabeledData&lt;InputType, unsigned int&gt;::element_type <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5b834dccb79cf2bd9623ad3caf374aa5">ElementType</a>;</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span> </div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a60fcdf9770f6f859edaead4ff9a2684e">  109</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_kernel_matrix.html" title="Kernel Gram matrix.">KernelMatrix&lt;InputType, QpFloatType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a60fcdf9770f6f859edaead4ff9a2684e">KernelMatrixType</a>;</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a86c3c80eefc4b570704a0d8bbc5a0f97">  110</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_partly_precomputed_matrix.html" title="Partly Precomputed version of a matrix for quadratic programming.">PartlyPrecomputedMatrix&lt; KernelMatrixType &gt;</a> <a class="code hl_typedef" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a86c3c80eefc4b570704a0d8bbc5a0f97">PartlyPrecomputedMatrixType</a>;</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span> </div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span> </div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span><span class="comment"></span> </div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment">    /// preinitialization methods</span></div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672ea3481ccda012741ec6f194790c8fa81b2">  115</a></span><span class="comment"></span>    <span class="keyword">enum</span> <a class="code hl_enumeration" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672e" title="preinitialization methods">preInitializationMethod</a> {<a class="code hl_enumvalue" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672ea3481ccda012741ec6f194790c8fa81b2">NONE</a>, <a class="code hl_enumvalue" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672eaa780ebd48025014f013b4fa68cc4ae45">RANDOM</a>}; <span class="comment">// TODO: add KMEANS</span></div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span> </div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span> </div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment"></span> </div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment">    /// \brief Constructor</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="comment">    /// Note that there is no cache size involved, as merging vectors will always create new ones,</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment">    /// which makes caching roughly obsolete.</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment">    /// \param[in]  kernel          kernel function to use for training and prediction</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    /// \param[in]  loss            (sub-)differentiable loss function</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment">    /// \param[in]  C               regularization parameter - always the &#39;true&#39; value of C, even when unconstrained is set</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment">    /// \param[in]  offset          whether to train with offset/bias parameter or not</span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment">    /// \param[in]  unconstrained   when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment">    /// \param[in]  budgetSize  size of the budget/model that the final solution will have. Note that it might be smaller though.</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    /// \param[in]  budgetMaintenanceStrategy   object that contains the logic for maintaining the budget size.</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// \param[in]  epochs      number of epochs the SGD solver should run. if zero is given, the size will be the max of 10*datasetsize or C*datasetsize</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment">    /// \param[in]  preInitializationMethod     the method to preinitialize the budget.</span></div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span><span class="comment">    /// \param[in]  minMargin   the margin every vector has to obey. Usually this is 1.</span></div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="comment">    ///</span></div>
<div class="foldopen" id="foldopen00134" data-start="{" data-end="}">
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aab286ceb068bacf0dcdd21b43c48c231">  134</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aab286ceb068bacf0dcdd21b43c48c231" title="Constructor Note that there is no cache size involved, as merging vectors will always create new ones...">KernelBudgetedSGDTrainer</a>(</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5a0280688fb9600606ea098dfa9fb3a5" title="get the kernel function">kernel</a>,</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html">LossType</a>* loss,</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a55681941faf8293971193dce813c5651" title="return the value of the regularization parameter">C</a>,</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="keywordtype">bool</span> offset,</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>,</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a1f1e8961c4e22246f2fe3d023335c571">budgetSize</a> = 500,</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        <a class="code hl_class" href="classshark_1_1_abstract_budget_maintenance_strategy.html" title="This is the abstract interface for any budget maintenance strategy.">AbstractBudgetMaintenanceStrategy&lt;InputType&gt;</a> *<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aacf277842f28d9d49b1aea197ba710ac">budgetMaintenanceStrategy</a> = NULL,</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a512de58a73a790ce3159452b73c1f892">epochs</a> = 1,</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <span class="keywordtype">size_t</span> <a class="code hl_enumeration" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672e" title="preinitialization methods">preInitializationMethod</a> = <a class="code hl_enumvalue" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672ea3481ccda012741ec6f194790c8fa81b2">NONE</a>,</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a7711ed0cef0489dc1bca0a9cb26e12fa">minMargin</a> = 1.0f</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>    ): <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>(<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5a0280688fb9600606ea098dfa9fb3a5" title="get the kernel function">kernel</a>)</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab625d21676430dfcbab2dfce0d0a71f2" title="pointer to loss function">m_loss</a>(loss)</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a>(<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a55681941faf8293971193dce813c5651" title="return the value of the regularization parameter">C</a>)</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a745df296984693159dcd99dce89e3d7f" title="should the resulting model have an offset term?">m_offset</a>(offset)</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a8cdbe89d6b1e2890ef965f6fd01a747e" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>(unconstrained)</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a>(<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a1f1e8961c4e22246f2fe3d023335c571">budgetSize</a>)</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a02e70a83231e2f686b861b9d353ffe23">m_budgetMaintenanceStrategy</a>(<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aacf277842f28d9d49b1aea197ba710ac">budgetMaintenanceStrategy</a>)</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a70e4a98aa96a4a85f15dd0fecb2956f1" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a>(<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a512de58a73a790ce3159452b73c1f892">epochs</a>)</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a014dcb01ea4274f8f7fc83887573c769">m_preInitializationMethod</a>(<a class="code hl_enumeration" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672e" title="preinitialization methods">preInitializationMethod</a>)</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    , <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ace0f955650ddfcfcec23ed5eb3d33307">m_minMargin</a>(<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a7711ed0cef0489dc1bca0a9cb26e12fa">minMargin</a>)</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    {</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span> </div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="comment">// check that the maintenance strategy is not null.</span></div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a02e70a83231e2f686b861b9d353ffe23">m_budgetMaintenanceStrategy</a>, <span class="stringliteral">&quot;Budget maintenance strategy must not be NULL!&quot;</span>);</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>, <span class="stringliteral">&quot;Kernel must not be NULL!&quot;</span>);</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab625d21676430dfcbab2dfce0d0a71f2" title="pointer to loss function">m_loss</a>, <span class="stringliteral">&quot;Loss must not be NULL!&quot;</span>);</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>    }</div>
</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span> </div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span><span class="comment"></span> </div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span><span class="comment">    /// get budget size</span></div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="comment">    /// \return     budget size</span></div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span><span class="comment">    ///</span></div>
<div class="foldopen" id="foldopen00167" data-start="{" data-end="}">
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a1f1e8961c4e22246f2fe3d023335c571">  167</a></span><span class="comment"></span>    <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a1f1e8961c4e22246f2fe3d023335c571">budgetSize</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a>;</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    }</div>
</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span> </div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span><span class="comment"></span> </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span><span class="comment">    /// set budget size</span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span><span class="comment">    /// \param[in]  budgetSize  size of budget.</span></div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment">    ///</span></div>
<div class="foldopen" id="foldopen00176" data-start="{" data-end="}">
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ae45eb76dbab8216393c04a45272c078a">  176</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ae45eb76dbab8216393c04a45272c078a">setBudgetSize</a>(std::size_t <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a1f1e8961c4e22246f2fe3d023335c571">budgetSize</a>)</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>    {</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a> = <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a1f1e8961c4e22246f2fe3d023335c571">budgetSize</a>;</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>    }</div>
</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span> </div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="comment"></span> </div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span><span class="comment">    /// return pointer to the budget maintenance strategy</span></div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span><span class="comment">    /// \return pointer to the budget maintenance strategy.</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span><span class="comment">    ///</span></div>
<div class="foldopen" id="foldopen00185" data-start="{" data-end="}">
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aacf277842f28d9d49b1aea197ba710ac">  185</a></span><span class="comment"></span>    <a class="code hl_class" href="classshark_1_1_abstract_budget_maintenance_strategy.html" title="This is the abstract interface for any budget maintenance strategy.">AbstractBudgetMaintenanceStrategy&lt;InputType&gt;</a> *<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aacf277842f28d9d49b1aea197ba710ac">budgetMaintenanceStrategy</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        <span class="keywordflow">return</span> (<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a02e70a83231e2f686b861b9d353ffe23">m_budgetMaintenanceStrategy</a>);</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    }</div>
</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span> </div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span><span class="comment"></span> </div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span><span class="comment">    /// set budget maintenance strategy</span></div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span><span class="comment">    /// \param[in]  budgetMaintenanceStrategy   set strategy to given object.</span></div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span><span class="comment">    ///</span></div>
<div class="foldopen" id="foldopen00194" data-start="{" data-end="}">
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a4f87b77aab4f3bc93ab429994d51caf3">  194</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a4f87b77aab4f3bc93ab429994d51caf3">setBudgetMaintenanceStrategy</a>(<a class="code hl_class" href="classshark_1_1_abstract_budget_maintenance_strategy.html" title="This is the abstract interface for any budget maintenance strategy.">AbstractBudgetMaintenanceStrategy&lt;InputType&gt;</a> *<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aacf277842f28d9d49b1aea197ba710ac">budgetMaintenanceStrategy</a>)</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>    {</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a02e70a83231e2f686b861b9d353ffe23">m_budgetMaintenanceStrategy</a> = <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aacf277842f28d9d49b1aea197ba710ac">budgetMaintenanceStrategy</a>;</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>    }</div>
</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span> </div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span><span class="comment"></span> </div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span><span class="comment">    /// return min margin</span></div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span><span class="comment">    /// \return     current min margin</span></div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span><span class="comment">    ///</span></div>
<div class="foldopen" id="foldopen00203" data-start="{" data-end="}">
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a7711ed0cef0489dc1bca0a9cb26e12fa">  203</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a7711ed0cef0489dc1bca0a9cb26e12fa">minMargin</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ace0f955650ddfcfcec23ed5eb3d33307">m_minMargin</a>;</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    }</div>
</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span> </div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span><span class="comment"></span> </div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span><span class="comment">    /// set min margin</span></div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="comment">    /// \param[in]  minMargin   new min margin.</span></div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span><span class="comment">    ///</span></div>
<div class="foldopen" id="foldopen00212" data-start="{" data-end="}">
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a9f083a7f478269c13cf1550e3fe0c469">  212</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a9f083a7f478269c13cf1550e3fe0c469">setMinMargin</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a7711ed0cef0489dc1bca0a9cb26e12fa">minMargin</a>)</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>    {</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ace0f955650ddfcfcec23ed5eb3d33307">m_minMargin</a> = <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a7711ed0cef0489dc1bca0a9cb26e12fa">minMargin</a>;</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>    }</div>
</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span> </div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span><span class="comment"></span> </div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00219" data-start="{" data-end="}">
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6479254636b6c638beec35a3ba495f5a">  219</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6479254636b6c638beec35a3ba495f5a" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        <span class="keywordflow">return</span> <span class="stringliteral">&quot;KernelBudgetedSGDTrainer&quot;</span>;</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>    }</div>
</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span> </div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span><span class="comment"></span> </div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span><span class="comment">    /// Train routine.</span></div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span><span class="comment">    /// \param[in]  classifier      classifier object for the final solution.</span></div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span><span class="comment">    /// \param[in]  dataset     dataset to work with.</span></div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span><span class="comment">    ///</span></div>
<div class="foldopen" id="foldopen00229" data-start="{" data-end="}">
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#afc6eff8c84cf39de20aaec579f1716b0">  229</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#afc6eff8c84cf39de20aaec579f1716b0">train</a>(<a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">ClassifierType</a> &amp;classifier, <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> &amp;dataset)</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>    {</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span> </div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>        std::size_t ell = dataset.<a class="code hl_function" href="group__shark__globals.html#ga5333445992cd6b14392cd80a1ab5403c" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>        std::size_t classes = <a class="code hl_function" href="group__shark__globals.html#ga1fee3b5830ae11a78109e8c0265c6569" title="Return the number of classes of a set of class labels with unsigned int label encoding.">numberOfClasses</a>(dataset);</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span> </div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>        <span class="comment">// is the budget size larger than reasonable?</span></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a> &gt; ell)</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>        {</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>            <span class="comment">// in this case we just set the budgetSize to the given dataset size, so basically</span></div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>            <span class="comment">// there is an infinite budget.</span></div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>            <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a> = ell;</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        }</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span> </div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        <span class="comment">// we always need one budget vector more than the user specified,</span></div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        <span class="comment">// as we first have to add any new vector to the budget before applying</span></div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>        <span class="comment">// the maintenance strategy. an alternative would be to keep the budget size</span></div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        <span class="comment">// correct and test explicitely for the new support vector, but that would</span></div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        <span class="comment">// create even more hassle on the other side. or one could use a vector of</span></div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>        <span class="comment">// budget vectors instead, but loose the nice framework of kernel expansions.</span></div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        <span class="comment">// so the last budget vector must always have zero alpha coefficients in</span></div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        <span class="comment">// the final model. (we do not check for that but roughly assume that in</span></div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        <span class="comment">// the strategies, e.g. by putting the new vector to the last position in the</span></div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>        <span class="comment">// merge strategy).</span></div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a> = <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a> + 1;</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span> </div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        <span class="comment">// easy access</span></div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        UIntVector y = <a class="code hl_function" href="namespaceshark.html#a5478d144c4c997faf5c246dd8e2f85b8" title="creates a batch from a range of inputs">createBatch</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>());</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span> </div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        <span class="comment">// create a preinitialized budget.</span></div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>        <span class="comment">// this is used to initialize the kernelexpansion, we will work with.</span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> preinitializedBudgetVectors(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a>, dataset.<a class="code hl_function" href="group__shark__globals.html#gaec57b5f22b3e8d2d67ad4b621f30fd54">element</a>(0));</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span> </div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        <span class="comment">// preinit the vectors first</span></div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        <span class="comment">// we still preinit even for no preinit, as we need the vectors in the</span></div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        <span class="comment">// constructor of the kernelexpansion. the alphas will be set to zero for none.</span></div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        <span class="keywordflow">if</span>((<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a014dcb01ea4274f8f7fc83887573c769">m_preInitializationMethod</a> == <a class="code hl_enumvalue" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672eaa780ebd48025014f013b4fa68cc4ae45">RANDOM</a>) || (<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a014dcb01ea4274f8f7fc83887573c769">m_preInitializationMethod</a> == <a class="code hl_enumvalue" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672ea3481ccda012741ec6f194790c8fa81b2">NONE</a>))</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        {</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>            <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> j = 0; j &lt; <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a>; j++)</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>            {</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>                <span class="comment">// choose a random vector</span></div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>                std::size_t b = <a class="code hl_function" href="namespaceshark_1_1random.html#aa64d4174eaf7111b03e0504eaa56b666" title="Draws a discrete number in {low,low+1,...,high} by drawing random numbers from rng.">random::discrete</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, std::size_t(0), ell - 1);</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span> </div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>                <span class="comment">// copy over the vector</span></div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>                preinitializedBudgetVectors.<a class="code hl_function" href="group__shark__globals.html#gaec57b5f22b3e8d2d67ad4b621f30fd54">element</a>(j) = dataset.<a class="code hl_function" href="group__shark__globals.html#gaec57b5f22b3e8d2d67ad4b621f30fd54">element</a>(b);</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>            }</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>        }</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span> </div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>        <span class="comment">/*</span></div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span><span class="comment">        // TODO: kmeans initialization</span></div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span><span class="comment">        if (m_preInitializationMethod == KMEANS) {</span></div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span><span class="comment">            // the negative examples individually. the number of clusters should</span></div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span><span class="comment">            // then follow the ratio of the classes. then we can set the alphas easily.</span></div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span><span class="comment">            // TODO: do this multiclass</span></div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span><span class="comment">            // TODO: maybe Kmedoid makes more sense because of the alphas.</span></div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span><span class="comment">            // TODO: allow for different maxiters</span></div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span><span class="comment">            Centroids centroids;</span></div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span><span class="comment">            size_t maxIterations = 50;</span></div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span><span class="comment">            kMeans (dataset.inputs(), m_budgetSize, centroids, maxIterations);</span></div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span><span class="comment"></span> </div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span><span class="comment">            // copy over to our budget</span></div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span><span class="comment">            Data&lt;RealVector&gt; const&amp; c = centroids.centroids();</span></div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span><span class="comment"></span> </div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span><span class="comment">            for (size_t j = 0; j &lt; m_budgetSize; j++) {</span></div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span><span class="comment">                preinitializedBudgetVectors.inputs().element (j) = c.element (j);</span></div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span><span class="comment">                preinitializedBudgetVectors.labels().element (j) = 1; //FIXME</span></div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span><span class="comment">            }</span></div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span><span class="comment">        }</span></div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span><span class="comment">        */</span></div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span> </div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>        <span class="comment">// budget is a kernel expansion in its own right</span></div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>        <a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">ModelType</a> &amp;budgetModel = classifier.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>();</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>        RealMatrix &amp;budgetAlpha = budgetModel.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>();</div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>        budgetModel.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a38c97766f52bf00e5b0120c46c15f37f">setStructure</a>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>, preinitializedBudgetVectors.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(), <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a745df296984693159dcd99dce89e3d7f" title="should the resulting model have an offset term?">m_offset</a>, classes);</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span> </div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span> </div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>        <span class="comment">// variables</span></div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span>        <span class="keyword">const</span> <span class="keywordtype">double</span> lambda = 1.0 / (ell * <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a>);</div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span>        std::size_t iterations;</div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span> </div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span> </div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>        <span class="comment">// set epoch number</span></div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a70e4a98aa96a4a85f15dd0fecb2956f1" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a> == 0)</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>            iterations = std::max(10 * ell, std::size_t (std::ceil(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a> * ell)));</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>            iterations = <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a70e4a98aa96a4a85f15dd0fecb2956f1" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a> * ell;</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span> </div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span> </div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>        <span class="comment">// set the initial alphas (we do this here, after the array has been initialized by setStructure)</span></div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a014dcb01ea4274f8f7fc83887573c769">m_preInitializationMethod</a> == <a class="code hl_enumvalue" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672eaa780ebd48025014f013b4fa68cc4ae45">RANDOM</a>)</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>        {</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>            <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> j = 0; j &lt; <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a>; j++)</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>            {</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>                <span class="keywordtype">size_t</span> c = preinitializedBudgetVectors.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(j);</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>                budgetAlpha(j, c) = 1 / (1 + lambda);</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>                budgetAlpha(j, (c + 1) % classes) = -1 / (1 + lambda);</div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>            }</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>        }</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span> </div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span> </div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>        <span class="comment">// whatever strategy we did use-- the last budget vector needs</span></div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>        <span class="comment">// to be zeroed out, either it was zero anyway (none preinit)</span></div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>        <span class="comment">// or it is the extra budget vector we need for technical reasons</span></div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>        row(budgetAlpha, <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a> - 1) *= 0;</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span> </div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span> </div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>        <span class="comment">// preinitialize everything to prevent costly memory allocations in the loop</span></div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span>        RealVector predictions(classes, 0.0);</div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span>        RealVector derivative(classes, 0.0);</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span> </div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span> </div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>        <span class="comment">// SGD loop</span></div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>        std::size_t b = 0;</div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span> </div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span>        <span class="keywordflow">for</span>(std::size_t iter = 0; iter &lt; iterations; iter++)</div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span>        {</div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span>            <span class="comment">// active variable</span></div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span>            b = <a class="code hl_function" href="namespaceshark_1_1random.html#aa64d4174eaf7111b03e0504eaa56b666" title="Draws a discrete number in {low,low+1,...,high} by drawing random numbers from rng.">random::discrete</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, std::size_t(0), ell - 1);</div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span> </div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>            <span class="comment">// for smaller datasets instead of choosing randomly a sample</span></div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>            <span class="comment">// permuting the dataset can be a valid strategy. We do not implement</span></div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>            <span class="comment">// that here.</span></div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span> </div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>            <span class="comment">// compute prediction within the budgeted model</span></div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>            <span class="comment">// this will compute the predictions for all classes in one step</span></div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span>            budgetModel.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a9a55c5a7f5e4b7f447f299c1b19050e2" title="Standard interface for evaluating the response of the model to a batch of patterns.">eval</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>().<a class="code hl_function" href="group__shark__globals.html#ga0ea72a74a21d5ff59772516b83c4a58b">element</a>(b), predictions);</div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span> </div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span>            <span class="comment">// now we follow the crammer-singer model as written</span></div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span>            <span class="comment">// in paper (p. 11 top), we compute the scores of the true</span></div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span>            <span class="comment">// class and the runner-up class. for the latter we remove</span></div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno">  359</span>            <span class="comment">// our true prediction temporarily and redo the argmax.</span></div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span> </div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span>            RealVector predictionsCopy = predictions;</div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span>            <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> trueClass = y[b];</div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span>            <span class="keywordtype">double</span> scoreOfTrueClass = predictions[trueClass];</div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span>                        predictions[trueClass] = -std::numeric_limits&lt;double&gt;::infinity();</div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span>            <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> runnerupClass = (<span class="keywordtype">unsigned</span> int)arg_max(predictions);</div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno">  366</span>            <span class="keywordtype">double</span> scoreOfRunnerupClass = predictions[runnerupClass];</div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span> </div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span>            <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(trueClass != runnerupClass);</div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span> </div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span>            <span class="comment">// scale alphas</span></div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>            budgetModel.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>() *= ((<span class="keywordtype">long</span> double)(1.0 - 1.0 / (iter + 1.0)));</div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span> </div>
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno">  373</span>            <span class="comment">// check if there is a margin violation</span></div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span>            <span class="keywordflow">if</span>(scoreOfTrueClass - scoreOfRunnerupClass &lt; <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ace0f955650ddfcfcec23ed5eb3d33307">m_minMargin</a>)</div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>            {</div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span>                <span class="comment">// TODO: check if the current vector is already part of our budget</span></div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span> </div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span>                <span class="comment">// as we do not use the predictions anymore, we use them to push the new alpha values</span></div>
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno">  379</span>                <span class="comment">// to the budgeted model</span></div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>                predictions.clear();</div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span> </div>
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno">  382</span>                <span class="comment">// set the alpha values (see p 11, beta_t^{(i)} formula in wang, crammer, vucetic)</span></div>
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno">  383</span>                <span class="comment">// alpha of true class</span></div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span>                predictions[trueClass] = 1.0 / ((<span class="keywordtype">long</span> double)(iter + 1.0) * lambda);</div>
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno">  385</span> </div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span>                <span class="comment">// alpha of runnerup class</span></div>
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno">  387</span>                predictions[runnerupClass] = -1.0 / ((<span class="keywordtype">long</span> double)(iter + 1.0) * lambda);</div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span> </div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span>                <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a02e70a83231e2f686b861b9d353ffe23">m_budgetMaintenanceStrategy</a>-&gt;addToModel(budgetModel, predictions, dataset.<a class="code hl_function" href="group__shark__globals.html#gaec57b5f22b3e8d2d67ad4b621f30fd54">element</a>(b));</div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno">  390</span>            }</div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span>        }</div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span> </div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno">  393</span>        <span class="comment">// finally we need to get rid of zero supportvectors.</span></div>
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno">  394</span>        budgetModel.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a503aaebca6ce5e7d8a6f79e5e039bd9f">sparsify</a>();</div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span> </div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno">  396</span>    }</div>
</div>
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno">  397</span><span class="comment"></span> </div>
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno">  398</span><span class="comment">    /// Return the number of training epochs.</span></div>
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno">  399</span><span class="comment">    /// A value of 0 indicates that the default of max(10, C) should be used.</span></div>
<div class="foldopen" id="foldopen00400" data-start="{" data-end="}">
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a512de58a73a790ce3159452b73c1f892">  400</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a512de58a73a790ce3159452b73c1f892">epochs</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno">  401</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno">  402</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a70e4a98aa96a4a85f15dd0fecb2956f1" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a>;</div>
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno">  403</span>    }</div>
</div>
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno">  404</span><span class="comment"></span> </div>
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno">  405</span><span class="comment">    /// Set the number of training epochs.</span></div>
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno">  406</span><span class="comment">    /// A value of 0 indicates that the default of max(10, C) should be used.</span></div>
<div class="foldopen" id="foldopen00407" data-start="{" data-end="}">
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ad5bd681e0b7e4f64f8ef3f031fe15396">  407</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ad5bd681e0b7e4f64f8ef3f031fe15396">setEpochs</a>(std::size_t value)</div>
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno">  408</span>    {</div>
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno">  409</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a70e4a98aa96a4a85f15dd0fecb2956f1" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a> = value;</div>
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno">  410</span>    }</div>
</div>
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno">  411</span><span class="comment"></span> </div>
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno">  412</span><span class="comment">    /// get the kernel function</span></div>
<div class="foldopen" id="foldopen00413" data-start="{" data-end="}">
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5a0280688fb9600606ea098dfa9fb3a5">  413</a></span><span class="comment"></span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a> *<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5a0280688fb9600606ea098dfa9fb3a5" title="get the kernel function">kernel</a>()</div>
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno">  414</span>    {</div>
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno">  415</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>;</div>
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno">  416</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno">  417</span><span class="comment">    /// get the kernel function</span></div>
<div class="foldopen" id="foldopen00418" data-start="{" data-end="}">
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#abe6e3c9bc8d82159fde4d0d6ce77a29c">  418</a></span><span class="comment"></span>    <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a> *<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#abe6e3c9bc8d82159fde4d0d6ce77a29c" title="get the kernel function">kernel</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno">  419</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno">  420</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>;</div>
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno">  421</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno">  422</span><span class="comment">    /// set the kernel function</span></div>
<div class="foldopen" id="foldopen00423" data-start="{" data-end="}">
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a197676a5f5247401a286c9f2c54db720">  423</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a197676a5f5247401a286c9f2c54db720" title="set the kernel function">setKernel</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a> *<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5a0280688fb9600606ea098dfa9fb3a5" title="get the kernel function">kernel</a>)</div>
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno">  424</span>    {</div>
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno">  425</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a> = <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5a0280688fb9600606ea098dfa9fb3a5" title="get the kernel function">kernel</a>;</div>
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno">  426</span>    }</div>
</div>
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno">  427</span><span class="comment"></span> </div>
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno">  428</span><span class="comment">    /// check whether the parameter C is represented as log(C), thus,</span></div>
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno">  429</span><span class="comment">    /// in a form suitable for unconstrained optimization, in the</span></div>
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno">  430</span><span class="comment">    /// parameter vector</span></div>
<div class="foldopen" id="foldopen00431" data-start="{" data-end="}">
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#af9ee46318c066f68346a9f95b0a9d89b">  431</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#af9ee46318c066f68346a9f95b0a9d89b">isUnconstrained</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno">  432</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno">  433</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a8cdbe89d6b1e2890ef965f6fd01a747e" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>;</div>
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno">  434</span>    }</div>
</div>
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno">  435</span><span class="comment"></span> </div>
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno">  436</span><span class="comment">    /// return the value of the regularization parameter</span></div>
<div class="foldopen" id="foldopen00437" data-start="{" data-end="}">
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a55681941faf8293971193dce813c5651">  437</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a55681941faf8293971193dce813c5651" title="return the value of the regularization parameter">C</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno">  438</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno">  439</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a>;</div>
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno">  440</span>    }</div>
</div>
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno">  441</span><span class="comment"></span> </div>
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno">  442</span><span class="comment">    /// set the value of the regularization parameter (must be positive)</span></div>
<div class="foldopen" id="foldopen00443" data-start="{" data-end="}">
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a901e47531bcb1c010bbaa859a4666f11">  443</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a901e47531bcb1c010bbaa859a4666f11" title="set the value of the regularization parameter (must be positive)">setC</a>(<span class="keywordtype">double</span> value)</div>
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno">  444</span>    {</div>
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno">  445</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(value &gt; 0.0);</div>
<div class="line"><a id="l00446" name="l00446"></a><span class="lineno">  446</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a> = value;</div>
<div class="line"><a id="l00447" name="l00447"></a><span class="lineno">  447</span>    }</div>
</div>
<div class="line"><a id="l00448" name="l00448"></a><span class="lineno">  448</span><span class="comment"></span> </div>
<div class="line"><a id="l00449" name="l00449"></a><span class="lineno">  449</span><span class="comment">    /// check whether the model to be trained should include an offset term</span></div>
<div class="foldopen" id="foldopen00450" data-start="{" data-end="}">
<div class="line"><a id="l00450" name="l00450"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aed3f039e3cfb8b26115d75838453707b">  450</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#aed3f039e3cfb8b26115d75838453707b" title="check whether the model to be trained should include an offset term">trainOffset</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno">  451</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno">  452</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a745df296984693159dcd99dce89e3d7f" title="should the resulting model have an offset term?">m_offset</a>;</div>
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno">  453</span>    }</div>
</div>
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno">  454</span><span class="comment"></span> </div>
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno">  455</span><span class="comment">    ///\brief  Returns the vector of hyper-parameters.</span></div>
<div class="foldopen" id="foldopen00456" data-start="{" data-end="}">
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ae4573dfb8f348f21bc95c74eddc97b48">  456</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ae4573dfb8f348f21bc95c74eddc97b48" title="Returns the vector of hyper-parameters.">parameterVector</a>()<span class="keyword"> const  </span>{</div>
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno">  457</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a8cdbe89d6b1e2890ef965f6fd01a747e" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>)</div>
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno">  458</span>            <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#afaa2ba692ab64a0edbff60d7ee6794db" title="Return the parameter vector.">parameterVector</a>() | log(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a>);</div>
<div class="line"><a id="l00459" name="l00459"></a><span class="lineno">  459</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00460" name="l00460"></a><span class="lineno">  460</span>            <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#afaa2ba692ab64a0edbff60d7ee6794db" title="Return the parameter vector.">parameterVector</a>() | <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a>;</div>
<div class="line"><a id="l00461" name="l00461"></a><span class="lineno">  461</span>    }</div>
</div>
<div class="line"><a id="l00462" name="l00462"></a><span class="lineno">  462</span><span class="comment"></span> </div>
<div class="line"><a id="l00463" name="l00463"></a><span class="lineno">  463</span><span class="comment">    ///\brief  Sets the vector of hyper-parameters.</span></div>
<div class="foldopen" id="foldopen00464" data-start="{" data-end="}">
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5150b09e061a93c353990fef1c4bd0a2">  464</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a5150b09e061a93c353990fef1c4bd0a2" title="Sets the vector of hyper-parameters.">setParameterVector</a>(RealVector <span class="keyword">const</span> &amp;newParameters)</div>
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno">  465</span>    {</div>
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno">  466</span>        <span class="keywordtype">size_t</span> kp = <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>();</div>
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno">  467</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(newParameters.size() == kp + 1);</div>
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno">  468</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad" title="Set the parameter vector.">setParameterVector</a>(subrange(newParameters,0,kp));</div>
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno">  469</span>        <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a> = newParameters.back();</div>
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno">  470</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a8cdbe89d6b1e2890ef965f6fd01a747e" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>) <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a> = exp(<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a>);</div>
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno">  471</span>    }</div>
</div>
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno">  472</span><span class="comment"></span> </div>
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno">  473</span><span class="comment">    ///\brief Returns the number of hyper-parameters.</span></div>
<div class="foldopen" id="foldopen00474" data-start="{" data-end="}">
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a66b3bfe5605e929520855fe6d5b95584">  474</a></span><span class="comment"></span>    <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a66b3bfe5605e929520855fe6d5b95584" title="Returns the number of hyper-parameters.">numberOfParameters</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno">  475</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00476" name="l00476"></a><span class="lineno">  476</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>() + 1;</div>
<div class="line"><a id="l00477" name="l00477"></a><span class="lineno">  477</span>    }</div>
</div>
<div class="line"><a id="l00478" name="l00478"></a><span class="lineno">  478</span> </div>
<div class="line"><a id="l00479" name="l00479"></a><span class="lineno">  479</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00480" name="l00480"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8">  480</a></span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a> *<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a80076005339d5be37635648497fd8ee8" title="pointer to kernel function">m_kernel</a>;                     <span class="comment">///&lt; pointer to kernel function</span></div>
<div class="line"><a id="l00481" name="l00481"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab625d21676430dfcbab2dfce0d0a71f2">  481</a></span>    <span class="keyword">const</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html">LossType</a> *<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab625d21676430dfcbab2dfce0d0a71f2" title="pointer to loss function">m_loss</a>;                   <span class="comment">///&lt; pointer to loss function</span></div>
<div class="line"><a id="l00482" name="l00482"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f">  482</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6c9ddc984e879808a91364668b27da4f" title="regularization parameter">m_C</a>;                               <span class="comment">///&lt; regularization parameter</span></div>
<div class="line"><a id="l00483" name="l00483"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a745df296984693159dcd99dce89e3d7f">  483</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a745df296984693159dcd99dce89e3d7f" title="should the resulting model have an offset term?">m_offset</a>;                            <span class="comment">///&lt; should the resulting model have an offset term?</span></div>
<div class="line"><a id="l00484" name="l00484"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a8cdbe89d6b1e2890ef965f6fd01a747e">  484</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a8cdbe89d6b1e2890ef965f6fd01a747e" title="should C be stored as log(C) as a parameter?">m_unconstrained</a>;                     <span class="comment">///&lt; should C be stored as log(C) as a parameter?</span></div>
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno">  485</span> </div>
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno">  486</span>    <span class="comment">// budget size</span></div>
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">  487</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#adefa2c47996b1323231f69b96c0be778">m_budgetSize</a>;</div>
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno">  488</span> </div>
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno">  489</span>    <span class="comment">// budget maintenance strategy</span></div>
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a02e70a83231e2f686b861b9d353ffe23">  490</a></span>    <a class="code hl_class" href="classshark_1_1_abstract_budget_maintenance_strategy.html" title="This is the abstract interface for any budget maintenance strategy.">AbstractBudgetMaintenanceStrategy&lt;InputType&gt;</a> *<a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a02e70a83231e2f686b861b9d353ffe23">m_budgetMaintenanceStrategy</a>;</div>
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno">  491</span> </div>
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a70e4a98aa96a4a85f15dd0fecb2956f1">  492</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a70e4a98aa96a4a85f15dd0fecb2956f1" title="number of training epochs (sweeps over the data), or 0 for default = max(10, C)">m_epochs</a>;                     <span class="comment">///&lt; number of training epochs (sweeps over the data), or 0 for default = max(10, C)</span></div>
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno">  493</span> </div>
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno">  494</span>    <span class="comment">// method to preinitialize budget</span></div>
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a014dcb01ea4274f8f7fc83887573c769">  495</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a014dcb01ea4274f8f7fc83887573c769">m_preInitializationMethod</a>;</div>
<div class="line"><a id="l00496" name="l00496"></a><span class="lineno">  496</span> </div>
<div class="line"><a id="l00497" name="l00497"></a><span class="lineno">  497</span>    <span class="comment">// needed margin below which we update the model, also called beta sometimes</span></div>
<div class="line"><a id="l00498" name="l00498"></a><span class="lineno"><a class="line" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ace0f955650ddfcfcec23ed5eb3d33307">  498</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ace0f955650ddfcfcec23ed5eb3d33307">m_minMargin</a>;</div>
<div class="line"><a id="l00499" name="l00499"></a><span class="lineno">  499</span>};</div>
</div>
<div class="line"><a id="l00500" name="l00500"></a><span class="lineno">  500</span> </div>
<div class="line"><a id="l00501" name="l00501"></a><span class="lineno">  501</span>}</div>
<div class="line"><a id="l00502" name="l00502"></a><span class="lineno">  502</span><span class="preprocessor">#endif</span></div>
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